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Temperature protocols to guide selective self-assembly of competing structures.

Arunkumar Bupathy1, Daan Frenkel2, Srikanth Sastry3

  • 1Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.

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|February 15, 2022
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Summary
This summary is machine-generated.

This study designs multicomponent self-assembly mixtures to encode two structures, selectable via temperature. Minimal shared components and specific design principles enable selective retrieval of desired aggregates.

Keywords:
directed assemblyprogrammable matterself-assembly

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Biophysics

Background:

  • Multicomponent self-assembly allows encoding multiple structures with one set of components.
  • Selective retrieval often relies on pre-conditioning the system, not dynamic switching.

Purpose of the Study:

  • To explore inverse design principles for multicomponent self-assembly mixtures.
  • To create mixtures encoding two competing structures selectable via temperature protocols.

Main Methods:

  • Designing target structures with differing nucleation barriers and global stability.
  • Analyzing component pair overlap and unique components between structures.
  • Utilizing temperature protocols for selective structure retrieval.

Main Results:

  • Minimal overlap in neighboring component pairs between target structures is crucial to avoid spurious aggregates.
  • Inclusion of unique components is necessary, but maximizing library sharing enhances selectivity.
  • Temperature protocols achieve high selectivity for either target structure formation.
  • Secondary aggregation products, termed 'vestigial aggregates,' play a key role in improving selectivity.

Conclusions:

  • Inverse design principles enable the creation of multicomponent self-assembly systems with switchable functionality.
  • Temperature control offers a viable method for selective structure retrieval in complex mixtures.
  • Understanding and leveraging secondary aggregates is important for optimizing self-assembly systems.